HyperSync: Hyperbolic Feature Matching for Multi-Agent Spatial Synchronization

Multi-agent spatial synchronization is a critical prerequisite for cooperative perception and information fusion in autonomous driving. Existing methods typically establish cross-agent feature correspondences using metrics such as inner products and bilinear similarities in euclidean feature spaces, or perform association using object trajectories and motion-consistency constraints. When substantial viewpoint differences, incomplete observations, or only a limited number of co-visible objects exist among different agents, existing methods have limited ability to distinguish true correspondences, false correspondences, and outliers, making them prone to erroneous associations and consequently degrading the stability of relative pose estimation and spatial alignment. To address these challenges, we propose HyperSync, a multi-agent spatial synchronization method based on hyperbolic-space representations. By exploiting the negative curvature and nonlinear distance properties of hyperbolic space, HyperSync maps candidate matching features extracted from different agents into the Poincaré ball model and constructs a cross-agent feature similarity metric based on hyperbolic distance, thereby enhancing the discriminability of feature correspondences under weak-overlap and sparse-co-visibility conditions. On this basis, spatial alignment among multi-agent observations is achieved through correspondence filtering and relative pose estimation. The proposed method is evaluated on the OPV2V, V2X-Sim and DAIR-V2X datasets. Experimental results demonstrate that, compared with existing methods, the proposed method achieves more accurate feature matching and relative pose estimation and exhibits better robustness under substantial viewpoint differences and conditions with only a limited number of co-visible objects.